Certified Lean Six Sigma Green Belt

Chapter 1 introduces the instructor, overviews the main objectives for the course, including an outline of the chapters. This chapter then defines Lean /Six Sigma including history, focus, characteristics, the problem-solving formula, and key goals. Chapter 1 also discusses benefits of Six Sigma (centering the process and reducing variability), introduces the DMAIC Methodology, and the primary deliverables. The Voice of the Customer (qualitative and quantitative attributes and an example) are introduced next, followed with use of the Kano Analysis.

Chapter 2 dives deeper into the Define Phase towards more understanding of the process, translation of the VOC into Critical to Quality (CTQ) parameters critical to quality indicators. This chapter then discusses the Cost of Poor Quality, the Pareto Principles, and Six Sigma Metrics (for measure).

Chapter 3 introduces Project Chartering including the charter itself, the structure, and various selection criteria for the project. Chapter 3 also defines key questions to ask of stakeholders and leadership for project selection, reviews SMART Goals in relation to selected projects, and helps to identify projects, both ones with potential and ones to avoid. The Define Phase also identifies the role of leadership in terms of strategy, vision, budget, resources and implementation organizationally.

Chapter 4 discusses the Lean methodology how it runs parallel to Six Sigma but is used in tandem with it in different ways across all phases. This chapter starts with the history of Lean (Toyota Production Systems), details the 5 Principles of Lean Thinking, discusses the Value of Time (WIP and Cycle Time) and adds in bottleneck analysis and system cycle time. Chapter 4 then reviews the 8 Deadly Wastes and introduces Kaizen and its rules (including when to use and not to use). This chapter also includes 5S, Poke-Yoke, and Kanban Systems (Single-Bin, Side-by-Side, and Two-Bin Systems), then moves into the Standard of Work, Standard Operating Procedures (SOPs), Job Instructions (JIs) and the Skills Matrix.

Chapter 5 defines the purpose of the Measure Phase, the second phase of the DMAIC Methodology. It also introduces Process Mapping, Value-Stream Mapping, Cause-and Effect Diagrams (for root cause identification), and Failure Mode and Effects Analysis (FMEA).

Chapter 6 discusses Descriptive Statistics, Normal Distributions and Normality, and Graphic Tools and Visual Display of Data.

Chapter 7 explores the purpose and need for running the Measurement Systems Analysis and the different types of data that may be encountered. This chapter identifies and explains the two key types of MSAs; the Attribute Agreement Assessment, and the Gage R & R.

Chapter 8 defines the purpose of Process Capability Analysis and identifies the key outputs. This chapter reviews Specification Limits, Cpk (Short-term Process Capability) and Ppk (Long-Term Process Performance), and shares Process Capability Analysis Examples.

Chapter 9 defines the purpose of the Analysis Phase, identifies, and describes the use of the graphical tools, and introduces hypothesis testing for statistical data analysis including the use of the hypothesis test selection rubric.

Chapter 10 discusses sources and patterns of variation, including the main types of variation, analyzing variation using Multi-Vari Analysis methods, and the different distribution classes.

Chapter 11 overviews what inferential statistics are and describes how they are used and introduces data sampling including the data attributes, the amount of data you should be collecting (minimum sample size), and how to collect it (continuous and discreet). This chapter then discusses the Central Limit Theorem and how it works across different data and populations.

Chapter 12 introduces the various forms of hypothesis testing in the Analyze Phase and discusses the differences between Alpha and Beta risk. This chapter then details each type of hypothesis test (with examples) and then discusses the concept of practical VS statistical significance.

Chapter 13 discusses the hypothesis tests available if data is normal (as a result of a normality test). This chapter reviews the 1-Sample T-Test, 2-Sample T-Test, the ANOVA, and the Test for Equal Variance.

Chapter 14 discusses the hypothesis tests available if data is NOT normal. This chapter details the Mood’s Median, the Mann-Whitney, the Kruskal-Wallace, and the ChiSquare hypothesis tests. Chapter 14 also overviews the 1-Sample Sign Test, the 1- Sample Wilcoxon Test, and the Friedman Test.

Chapter 15 defines the purpose of the Improve Phase and understanding and assessing the nature of the Vital X’s including determining operating parameters and critical elements and determining and selecting improvement strategies. This chapter reviews prioritizing solutions to identify the most impactful in changing performance of the Vital X’s and introduces piloting to experiment and attain a proof of concept.

Chapter 16 provides an overview of what it means to perform a regression analysis and describes the use of the graphical tools (Scatterplots). This chapter discusses Correlation Theory, performing Linear Regression, and using Regression Equations and Residuals. Chapter 16 details advanced forms of regression including Multi-Variate Regression, and Non-linear Regression, as well as using Data Transformation to change
the regression approach/model.

Chapter 17 defines the purpose of the Control Phase and the necessity of performing a Cost Benefit Analysis including using Classifications and completing Break-Even Analysis. This chapter discusses Control and Response Planning which also allows for successfully closing the project, and reviews two Control Plan examples.

Chapter 18 overviews Statistical Process Control including exploring the various SPC types including I-MR, X-Bar – R, X-Bar—S, C—Chart, U—Chart, NP—Chart, and P— Chart. This chapter introduces Cumulative Sum (CuSum) and EMWA charts and how to interpret the profiles of the outputs of SPCs via Nelson’s Rules.

Chapter 19 looks to the future and discusses the implications of the Six Sigma methodology and how it will relate with emerging technologies from Industry 4.0. This chapter considers the alliance forming between Six Sigma and Industry 4.0 organizations to gain competitive advantage and become stronger in the realm of continuous improvement. Chapter 19 also reviews industry examples.

Chapter 20 describes the role of change management when applying Six Sigma and discusses why organizations need to change. This chapter introduces John Kotter and the 8-Stage Model, as well as the Change Formula.